The maps and analyses in this section are based on a corpus of 79,040 studies in the published scientific literature. The corpus is shared between chapter 3 and 4 of the values assessment. For more information about the corpus see IPBES VA Chapter 3. Systematic review on Method Families (https://doi.org/10.5281/zenodo.4404436) and IPBES VA Chapter 4. Systematic review on valuation uptake (https://doi.org/10.5281/zenodo.4391335).
To identify country names in the corpus of literature a two step approach was used. First, we wanted to understand where studies were conducted and searched the title, abstract, and keywords of each paper for country names. Second, to understand where the funding organizations were located we searched the affiliations, acknowledgments, and funding text for country names.
The input data we used are the following:
Bib file downloaded from Web of Science
ISO 3166-1 alpha-3 country (includes some territories) codes (https://www.iso.org/iso-3166-country-codes.html)
IPBES regional and subregional area dataset (https://doi.org/10.5281/zenodo.3923633)
The python code used to georeference the corpus can be found here. An overview of the pipeline is provided in the following schematic and described below.
knitr::include_graphics("pilot2.svg")
Overview of the process of Georeferencing the corpus of valuation studies
Step 1: Extract country names from text Country names were extracted from the title, abstract, and keywords of each paper with a regular expression and the associated ISO code was added into a a column in the dataset. The same regular expression was also used to search the affiliations, acknowledgments, and funding text of the same paper and placed into a second column.
Step 2 and 3: Bundle countries in regions and subregions The IPBES Regions and Subregions datatset was then used to add additional region and subregion attributes to the dataset by matching the ISO3 code.
Step 4: Find TS accordingly We used a set of files to add additional attributes to the dataset that identified the topics. The set of files contained identifying information for papers derived from sets of web of science searches targeting particular topics. This identifying information was then matched to the corpus, and the topic extracted. For more details on topic identification please see chapter 4 systematic review on valuation uptake (https://doi.org/10.5281/zenodo.4391335).
Finally, the complete corpus with the added attributes of country ISO codes of both funding institutions and research locations, and topic identification were used as the basis of the rest of the research project. The complete corpus can be found on Zenodo here: [https://doi.org/[INSERT](https://doi.org/%5BINSERT DOI]
The complete georeferenced valuation corpus was used to understand the location of valuation studies throughout the world and the location of the institutions conducting those studies.
We counted the number of times a country or territory was listed in the corpus from the column of country or territory names obtained from the title, abstract, and keywords search as a proxy for the density of valuation studies.
Separately, we counted the number of times a country or territory was listed in the corpus from the column of country or territory names obtained from the affiliations acknowledgments, and funding text as a proxy for the density of institutions.
data <- readxl::read_excel("Data/IPBES_VA_Uptake_Corpus_06May20_GEONames_TS_16June20.xlsx", sheet = 1)
n <- nrow(data)
t <- data %>%
mutate(x = strsplit(as.character(CountryName_TI_AB_DE_ID), ", ")) %>%
unnest(x) %>%
count(x, sort = TRUE)
studies_na <- t %>%
filter(is.na(x))
t_names2 <- data %>%
mutate(x = strsplit(as.character(CountryName_CI_FU_FX), ", ")) %>%
unnest(x) %>%
count(x, sort = TRUE)
names2_na <- t_names2 %>%
filter(is.na(x))
cat("Out of", n,"total studies,", studies_na$n, "studies did not identify a country or territory within their title, abstract, or keywords. ", "While", names2_na$n, "studies did not identify a country within their affiliations, acknowledgments, and funding text.")
Out of 79040 total studies, 30259 studies did not identify a country or territory within their title, abstract, or keywords. While 345 studies did not identify a country within their affiliations, acknowledgments, and funding text.
We will now present the results from the analysis through a series of maps showing both the raw country or territory results and summarized by IPBES regions and subregions. No data is always displayed in grey. The darker green values represent higher density of studies, while the darker blue values represent higher density of institutions.
# Declare Functions
countFunction <- function(data, x) {
data %>%
mutate(x = strsplit(as.character(x), ", ")) %>%
unnest(x) %>%
count(x, sort = TRUE) %>%
drop_na() %>%
mutate(n_log = log(n))
}
# Necessary datasets for the upcoming maps
harmonized_data <- read.csv("Outputs/Corpus/harmonized_data.csv")
indicators <- read.csv("Outputs/indicators_compiled.csv")
column_names <- readxl::read_excel("Data/names_of_columns.xlsx")
countries <- read_sf("Data/gadm36_levels_shp/gadm36_0.shp") %>%
rename(ISO_Alpha_3 = GID_0)
# Combine and project data
colnames(indicators)[2:25] <- column_names$Short_Name
df <- left_join(harmonized_data, indicators, by = "ISO_Alpha_3")
df <- left_join(countries, df, by = "ISO_Alpha_3")
# Project data
robin_crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
poly <- st_transform(df, robin_crs)
# Preparing Data
# all
percentage_VS_all <- st_drop_geometry(df) %>%
select(NAME_0, ISO_Alpha_3, Names1, Names2) %>%
mutate(Names1_percent = (Names1/sum(df$Names1, na.rm = T))) %>%
mutate(Names2_percent = (Names2/sum(df$Names2, na.rm = T))) %>%
arrange(desc(Names1_percent))
# Before 2010
## Create dataset
corpus_b2010 <- data %>%
filter(PY < 2010)
n1 <- corpus_b2010 %>%
countFunction(x = corpus_b2010$CountryName_TI_AB_DE_ID) %>%
rename("ISO_Alpha_3" = x, "Names1" = n, "Names1_log" = n_log)
n2 <- corpus_b2010 %>%
countFunction(x = corpus_b2010$CountryName_CI_FU_FX) %>%
rename("ISO_Alpha_3" = x, "Names2" = n, "Names2_log" = n_log)
df_b2010 <- full_join(n1, n2, by = "ISO_Alpha_3")
df_b2010_t <- left_join(countries, df_b2010, by = "ISO_Alpha_3")
## Project data
robin_crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
poly_b2010 <- st_transform(df_b2010_t, robin_crs)
percentage_VS_b2010 <- st_drop_geometry(df_b2010_t) %>%
select(NAME_0, ISO_Alpha_3, Names1, Names2) %>%
mutate(Names1_percent = (Names1/sum(df_b2010_t$Names1, na.rm = T))) %>%
mutate(Names2_percent = (Names2/sum(df_b2010_t$Names2, na.rm = T))) %>%
arrange(desc(Names1_percent))
# After 2010
data_2010 <- read.csv("Outputs/Corpus_2010/harmonized_data.csv")
df_2010 <- left_join(countries, data_2010, by = "ISO_Alpha_3")
robin_crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m"
poly_2010 <- st_transform(df_2010, robin_crs)
percentage_VS_a2010 <- st_drop_geometry(df_2010) %>%
select(NAME_0, ISO_Alpha_3, Names1, Names2) %>%
mutate(Names1_percent = (Names1/sum(df_2010$Names1, na.rm = T))) %>%
mutate(Names2_percent = (Names2/sum(df_2010$Names2, na.rm = T))) %>%
arrange(desc(Names1_percent))
# For plots
grid <- st_graticule(lat = seq(-90, 90, by = 30), # the graticules
lon = seq(-180, 180, by = 60)) %>%
st_transform("+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m") %>%
st_geometry
Showcases the number of valuation studies conducted within each country or territory for the entire dataset. Please note that the scales are not consistent between maps.
cat("There were",
sum(t$n) - studies_na$n,
"identifications of a country or territory from the title, abstract, or keywords, consisting of",
nrow(t) - nrow(studies_na),
"countries or territories identified.\n\n",
"\nThe United States has disproportionately higher valuation studies than other countries.", " In descending order,",
paste(percentage_VS_all$NAME_0[1]),
paste("(",round(percentage_VS_all$Names1_percent[1]*100, 2), "%),", sep = ""),
paste(percentage_VS_all$NAME_0[2]),
paste("(",round(percentage_VS_all$Names1_percent[2]*100, 2), "%),", sep = ""),
paste(percentage_VS_all$NAME_0[3]),
paste("(",round(percentage_VS_all$Names1_percent[3]*100, 2), "%),", sep = ""),
paste(percentage_VS_all$NAME_0[4]),
paste("(",round(percentage_VS_all$Names1_percent[4]*100, 2), "%) and", sep = ""),
paste(percentage_VS_all$NAME_0[5]),
paste("(",round(percentage_VS_all$Names1_percent[5]*100, 2), "%)", sep = ""),
"have the highest valuation studies in total and for studies post 2010."
)
There were 64688 identifications of a country or territory from the title, abstract, or keywords, consisting of 217 countries or territories identified.
The United States has disproportionately higher valuation studies than other countries. In descending order, United States (10.96%), China (6.66%), Australia (5.36%), Brazil (4.66%) and India (3.74%) have the highest valuation studies in total and for studies post 2010.
There was a large relative increase of valuation studies conducted in China before 2010 and after 2010. Before 2010, China comprised of 2.80% of the valuation studies, but after 2010 China comprised 8.04% of the valuation studies.
plot1 <- ggplot(poly) + # Names 1 all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_colour_manual(values = NA) +
guides(colour = guide_legend("No data", override.aes = list(colour = "grey", fill = "grey")))+
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom") +
scale_fill_gradient(
low = "#F7FCB9",
high = "#006837",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Studies")
# Names 1 Log
plot1_log <- ggplot(poly) + # Names 2 log all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1_log, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#F7FCB9",
high = "#006837",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Studies (log)") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_grid(plot1, plot1_log, labels = "auto", ncol = 1)
Showcases the number of valuation studies conducted within each country or territory in the dataset before 2010. Please note that the scales are not consistent between maps.
cat("Before 2010, the countries with the highest valuation studies conducted were the",
paste(percentage_VS_b2010$NAME_0[1]),
paste("(",round(percentage_VS_b2010$Names1_percent[1]*100, 2), "%),", sep = ""),
paste(percentage_VS_b2010$NAME_0[2]),
paste("(",round(percentage_VS_b2010$Names1_percent[2]*100, 2), "%),", sep = ""),
paste(percentage_VS_b2010$NAME_0[3]),
paste("(",round(percentage_VS_b2010$Names1_percent[3]*100, 2), "%),", sep = ""),
paste(percentage_VS_b2010$NAME_0[4]),
paste("(",round(percentage_VS_b2010$Names1_percent[4]*100, 2), "%) and", sep = ""),
paste(percentage_VS_b2010$NAME_0[5]),
paste("(",round(percentage_VS_b2010$Names1_percent[5]*100, 2), "%).", sep = "")
)
Before 2010, the countries with the highest valuation studies conducted were the United States (12.21%), Australia (6.37%), United Kingdom (5.17%), India (3.61%) and Canada (3.32%).
# Plotting Names 1
plotC <- ggplot(poly_b2010) + # Names 1 all studies before 2010
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_colour_manual(values = NA) +
guides(colour = guide_legend("No data", override.aes = list(colour = "grey", fill = "grey")))+
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom") +
scale_fill_gradient(
low = "#F7FCB9",
high = "#006837",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Studies")
# Plotting Names 1 log
plotC_log <- ggplot(poly_b2010) + # Names 2 log all studies before 2010
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1_log, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#F7FCB9",
high = "#006837",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Studies (log)") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_grid(plotC, plotC_log, labels = "auto", ncol = 1)
Showcases the number of valuation studies conducted within each country or territory in the dataset from 2010. Please note that the scales are not consistent between maps.
cat("In descending order, the",
paste(percentage_VS_a2010$NAME_0[1]),
paste("(",round(percentage_VS_a2010$Names1_percent[1]*100, 2), "%),", sep = ""),
paste(percentage_VS_a2010$NAME_0[2]),
paste("(",round(percentage_VS_a2010$Names1_percent[2]*100, 2), "%),", sep = ""),
paste(percentage_VS_a2010$NAME_0[3]),
paste("(",round(percentage_VS_a2010$Names1_percent[3]*100, 2), "%),", sep = ""),
paste(percentage_VS_a2010$NAME_0[4]),
paste("(",round(percentage_VS_a2010$Names1_percent[4]*100, 2), "%) and", sep = ""),
paste(percentage_VS_a2010$NAME_0[5]),
paste("(",round(percentage_VS_a2010$Names1_percent[5]*100, 2), "%)", sep = ""),
"have the highest valuation studies in total and for studies post 2010."
)
In descending order, the United States (10.49%), China (8.04%), Brazil (5.18%), Australia (5.01%) and India (3.8%) have the highest valuation studies in total and for studies post 2010.
# Plotting Names 1
plotA <- ggplot(poly_2010) + # Names 1 all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_colour_manual(values = NA) +
guides(colour = guide_legend("No data", override.aes = list(colour = "grey", fill = "grey")))+
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom") +
scale_fill_gradient(
low = "#F7FCB9",
high = "#006837",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Studies")
# Plotting Names 1 log
plotA_log <- ggplot(poly_2010) + # Names 2 log all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1_log, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#F7FCB9",
high = "#006837",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Studies (log)") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_grid(plotA, plotA_log, labels = "auto", ncol = 1)
Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the entire dataset as a proxy for institutions which are conducing the research. Please note that the scales are not consistent between maps.
percentage_VS_all <- percentage_VS_all %>%
arrange(desc(Names2_percent))
cat("There were",
sum(t_names2$n) - names2_na$n,
"identifications of a country or territory from the affiliations, acknowledgments, or funding text, consisting of",
nrow(t_names2) - nrow(names2_na),
"countries or territories.\n\n",
"\nThe United States and the United Kingdom (UK) have disproportionately higher density of institutions than other countries. In descending order, the",
paste(percentage_VS_all$NAME_0[1]),
paste("(",round(percentage_VS_all$Names2_percent[1]*100, 2), "%),", sep = ""),
paste(percentage_VS_all$NAME_0[2]),
paste("(",round(percentage_VS_all$Names2_percent[2]*100, 2), "%),", sep = ""),
paste(percentage_VS_all$NAME_0[3]),
paste("(",round(percentage_VS_all$Names2_percent[3]*100, 2), "%),", sep = ""),
paste(percentage_VS_all$NAME_0[4]),
paste("(",round(percentage_VS_all$Names2_percent[4]*100, 2), "%) and", sep = ""),
paste(percentage_VS_all$NAME_0[5]),
paste("(",round(percentage_VS_all$Names2_percent[5]*100, 2), "%)", sep = ""),
"have the highest density of institutions."
)
There were 140188 identifications of a country or territory from the affiliations, acknowledgments, or funding text, consisting of 210 countries or territories.
The United States and the United Kingdom (UK) have disproportionately higher density of institutions than other countries. In descending order, the United States (17.89%), United Kingdom (7.36%), Germany (5.19%), Australia (4.9%) and China (4.64%) have the highest density of institutions.
plot2 <- ggplot(poly) + # Names 2 all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names2, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#DEEBF7",
high = "#08519C",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Institutions") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
# Names 2 Log
plot2_log <- ggplot(poly) + # Names 2 log all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names2_log, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#DEEBF7",
high = "#08519C",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Institutions (log)") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_grid(plot2, plot2_log, labels = "auto", ncol = 1)
Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the studies in the dataset before 2010 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.
percentage_VS_b2010 <- percentage_VS_b2010 %>%
arrange(desc(Names2_percent))
cat("Before 2010, in descending order, the",
paste(percentage_VS_b2010$NAME_0[1]),
paste("(",round(percentage_VS_b2010$Names2_percent[1]*100, 2), "%),", sep = ""),
paste(percentage_VS_b2010$NAME_0[2]),
paste("(",round(percentage_VS_b2010$Names2_percent[2]*100, 2), "%),", sep = ""),
paste(percentage_VS_b2010$NAME_0[3]),
paste("(",round(percentage_VS_b2010$Names2_percent[3]*100, 2), "%),", sep = ""),
paste(percentage_VS_b2010$NAME_0[4]),
paste("(",round(percentage_VS_b2010$Names2_percent[4]*100, 2), "%) and", sep = ""),
paste(percentage_VS_b2010$NAME_0[5]),
paste("(",round(percentage_VS_b2010$Names2_percent[5]*100, 2), "%)", sep = ""),
"have the highest density of institutions."
)
Before 2010, in descending order, the United States (25.28%), United Kingdom (9.52%), Canada (5.23%), Australia (5.03%) and Germany (4.83%) have the highest density of institutions.
# Plotting Names 2
plotD <- ggplot(poly_b2010) + # Names 2 all studies before 2010
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names2, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#DEEBF7",
high = "#08519C",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Institutions") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
# Plotting Names 2 log
plotD_log <- ggplot(poly_b2010) + # Names 2 log all studies before 2010
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names2_log, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#DEEBF7",
high = "#08519C",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Institutions (log)") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_grid(plotD, plotD_log, labels = "auto", ncol = 1)
Showcases the number of studies which reference the particular country or territory within the affiliations, acknowledgments, or funding text for the studies in the dataset from 2010 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.
percentage_VS_a2010 <- percentage_VS_a2010 %>%
arrange(desc(Names2_percent))
cat("After 2010, in descending order, the",
paste(percentage_VS_a2010$NAME_0[1]),
paste("(",round(percentage_VS_a2010$Names2_percent[1]*100, 2), "%),", sep = ""),
paste(percentage_VS_a2010$NAME_0[2]),
paste("(",round(percentage_VS_a2010$Names2_percent[2]*100, 2), "%),", sep = ""),
paste(percentage_VS_a2010$NAME_0[3]),
paste("(",round(percentage_VS_a2010$Names2_percent[3]*100, 2), "%),", sep = ""),
paste(percentage_VS_a2010$NAME_0[4]),
paste("(",round(percentage_VS_a2010$Names2_percent[4]*100, 2), "%) and", sep = ""),
paste(percentage_VS_a2010$NAME_0[5]),
paste("(",round(percentage_VS_a2010$Names2_percent[5]*100, 2), "%)", sep = ""),
"have the highest density of institutions."
)
After 2010, in descending order, the United States (15.81%), United Kingdom (6.75%), China (5.39%), Germany (5.29%) and Australia (4.87%) have the highest density of institutions.
plotB <- ggplot(poly_2010) + # Names 2 all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names2, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#DEEBF7",
high = "#08519C",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Institutions") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
# Plotting Names 2 log
plotB_log <- ggplot(poly_2010) + # Names 2 log all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names2_log, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#DEEBF7",
high = "#08519C",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 5,
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Density of Institutions (log)") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_grid(plotB, plotB_log, labels = "auto", ncol = 1)
Europe and Central Asia region has the highest density of institutions, but comes in third for the highest density of studies. In contrast, the Americas region comes in first for the highest density of studies and second for institutions.
Besides Antarctica, Africa has the lowest density of studies and institutions.
knitr::include_graphics("Outputs/Maps/Names1_perregion.png")
region1_counts <- data %>%
mutate(x = strsplit(as.character(Region_TI_AB_DE_ID), ", ")) %>%
unnest(x) %>%
count(x, sort = TRUE)
region1_na <- region1_counts %>%
filter(is.na(x))
cat("* ",region1_na$n, "studies did not have a region identified for the density of studies.")
knitr::include_graphics("Outputs/Maps/Names2_perregion.png")
Europe and North America disproportionately have the highest density of studies and institutions between subregions, while Central Asia have the lowest density for both categories.
knitr::include_graphics("Outputs/Maps/Names1_persubregion.png")
subregion1_counts <- data %>%
mutate(x = strsplit(as.character(Subregion_TI_AB_DE_ID), ", ")) %>%
unnest(x) %>%
count(x, sort = TRUE)
subregion1_na <- subregion1_counts %>%
filter(is.na(x))
cat("* ",subregion1_na$n, "studies did not have a subregion identified for the density of studies.")
knitr::include_graphics("Outputs/Maps/Names2_persubregion.png")
OECD member countries and middle-income countries broadly had relatively more studies in their countries relative to research and funding affiliations; also Greenland, the Antarctic, some Latin American and Asian, most African countries had relatively more research and funding affiliations compared to the number of studies in their countries. Countries mostly in the southern hemisphere had a disproportionate number of researchers engaged in ecosystem services/NCP assessment studies, relative to the number of studies carried out in their country, in comparison to Northern hemisphere countries. Because these are ratios, it is better to consider them in the light of country/population size. From a Southern-country perspective, this could indicate: (i) high frequency of collaboration outside their own countries of researchers from countries in the South, and (ii) underfunding of domestic valuation studies in countries of the South relative to the number of domestic researchers and funding agencies. From the perspective of countries in the North this could indicate (i) relatively limited research collaboration outside their country for researchers from the North, and (ii) well-funded ecosystem services/NCP research relative to number of researchers and funding affiliations. From both these perspectives the narrative is of a relative export of ecosystem services/NCP research capacity from Southern countries to other countries. The data may also simply show a relative deficit of ecosystem services/NCP study applications in the South relative to the North. No data was found on cross-border flows of funding or researchers to make any further interpretation. This is a potential knowledge gap. More analysis is needed on whether there are patterns in relation to ecosystem assessment method families, and whether the patterns have changed over time. There are many exceptions to the broad global pattern which merit country-specific studies of the institutional context of ecosystem assessment to identify barriers and successful leverage points.
plot_1and2_ratio <- ggplot(poly) + # Names 2 log all studies
geom_sf(data = grid,
colour = "gray60",
linetype = "dashed") +
geom_sf(aes(fill = Names1/Names2, colour = NULL)) +
annotate("text", x = -18000000, y = 0, label = "0°", size = 3) +
annotate("text", x = -18000000, y = 3200000, label = "30° N", size = 3) +
annotate("text", x = -15500000, y = 6200000, label = "60° N", size = 3) +
annotate("text", x = -18000000, y = -3200000, label = "30° S", size = 3) +
annotate("text", x = -15500000, y = -6200000, label = "60° S", size = 3) +
annotate("text", x = 0, y = 9500000, label = "0°", size = 3) +
annotate("text", x = -3000000, y = 9500000, label = "60°W", size = 3) +
annotate("text", x = 3000000, y = 9500000, label = "60°E", size = 3) +
annotate("text", x = -8000000, y = 9500000, label = "180°W", size = 3) +
annotate("text", x = 8000000, y = 9500000, label = "180°E", size = 3) +
scale_fill_gradient(
low = "#F7FCB9",
high = "#FB8C00",
space = "Lab",
na.value = "grey",
aesthetics = "fill",
n.breaks = 6,
breaks = c(1,3,5,7,9,11),
guide = guide_colorbar(title.position = "top",
title.hjust = .5,
barwidth = 10,
barheight = 0.5
)) +
labs(fill = "Ratio of Studies and Institutions") +
theme(panel.background = element_blank(),
axis.text.x = element_text(size = 12),
axis.title = element_blank(),
legend.position = "bottom")
plot_1and2_ratio
It is clear that there is a significant positive linear relationship between the density of studies and institutions. It is important to note that there were many more identified countries for institutions than location of studies.
knitr::include_graphics("Outputs/Corpus/Names1_Names2.png")
Density of studies vs density of institutions
The timeline for the study application in the georeferenced countries shows a relatively uniform distribution of scientific literature in English across IPBES regions in the decade of the 1990s. The decade of the Millennium Ecosystem Assessment (2005-2015) saw a relative concentration of ecosystem services/NCP studies in a few countries (US, Brazil, India, UK). The decade including studies produced by The Economics of Ecosystems and Biodiversity - TEEB study (2008-2018) saw further concentration on those countries, and some diversification to EU countries, African countries and China. A hypothesis arising from this descriptive data is ‘first starter advantage’ in generating valuation capacity with the Millennium Ecosystem Assessment, with notable exceptions such as China and South Africa.
Finally, there has been an exponential increase in valuation studies from the 1980’s to the present as pictured in the graph below.
knitr::include_graphics("Outputs/Corpus/Timeline.svg")
Additionally, we explored how the density of valuation studies has changed throughout the last four decades. No data is displayed in grey.
Pre 1990 within the corpus, there are four countries represented (USA, Brazil, Norway, and Sweden) with a total of 5 identifications of a country or territory. All countries had only one identification, except for the USA with 2.
knitr::include_graphics("Outputs/Maps/temporal/Names1_1980-1990.png")
Between 1990 and 2000 within the corpus, there are 153 countries or territories represented with a total of 2,704 identifications, three orders of magnitude larger than the number of identifications before 1990.
knitr::include_graphics("Outputs/Maps/temporal/Names1_1990-2000.png")
Between 2000 and 2010 within the corpus, there are 200 countries or territories represented with a total of 14,644 identifications, again an order of magnitude increase from before 2000.
knitr::include_graphics("Outputs/Maps/temporal/Names1_2000-2010.png")
2010 and later within the corpus, there are 214 countries or territories represented with a total of 46,918 identifications.
knitr::include_graphics("Outputs/Maps/temporal/Names1_2010-2020.png")
Additionally, we explored how the density of valuation institutions has changed through time throughout the last four decades. No data is displayed in gray.
Pre 1990 within the corpus, there are eight countries or territories represented with a total of 24 identifications of a country or territory. USA had 14 identifications, Canada 3 identifications, Norway 2 identifications, while the rest of the represented countries had 1.
knitr::include_graphics("Outputs/Maps/temporal/Names2_1980-1990.png")
Between 1990 and 2000 within the corpus, there are 110 countries or territories represented with a total of 4,461 identifications of a country or territory, two orders of magnitude larger than pre 1990.
knitr::include_graphics("Outputs/Maps/temporal/Names2_1990-2000.png")
Between 2000 and 2010 within the corpus, there are 179 countries or territories represented with a total of 26,476 identifications of a country or territory, an order of magnitude larger than pre 2000.
knitr::include_graphics("Outputs/Maps/temporal/Names2_2000-2010.png")
2010 and later within the corpus, there are 207 countries or territories represented with a total of 108,101 identifications of a country or territory.
knitr::include_graphics("Outputs/Maps/temporal/Names2_2010-2020.png")
The IPBES Core Indicators were used alongside a chosen set of other relevant indicators to understand geographic trends between density of valuation studies and how they relate to biological and socioeconomic indicators.
We used all the most recent year of the IPBES Core Indicators available within the country dataset except for two indicators, Countries/Regions with Active NBSAP and Category 1 nations in CTIES, as these are binary in the dataset and would not be compatible with the following analysis. We selected a specific category from the indicators with multiple categories. For example, for the indicator “Area of forest production under FSC and PEFC certification” we chose the FSC certification area and not the PEFC certification area.
A set of other indicators were included in the analysis to expand the coverage of socioeconomic variables. We included the human development index (HDI), average harmonized learning outcomes score, gross domestic product (GDP), corruption perception index (CPI), and population.
These datasets were downloaded, cleaned, and had ISO3 codes added to easily merge them into the analysis. The latest data available was used for each indicator.
Here is the table of all of the indicators used, the category selected, the year the data is from, and the number assigned to them.
| Name | Category | Year |
|---|---|---|
| Forest Area Under FSC and PEFC Certification | FSC_area | 2016 |
| Biodiversity Habitat Index | Average | 2014 |
| Biodiversity Intactness Index | Value | 2005 |
| Biocapacity Per Capita | Value - Total | 2012 |
| Ecological Footprint Per Capita | Value - Total | 2012 |
| Forest Area | Forest area (1000ha) | 2015 |
| Water Footprint | Water Footprint - Total (Mm3/y) | 2013 |
| Inland Fishery Production | Capture | 2015 |
| Region-based Marine Trophic Index | 1950 | 2014 |
| Nitrogen + Phosphate Fertilizers | N total nutrients - Consumption in nutrients | 2014 |
| Percent Nitrogen Use Efficiency | Nitrogen Use Efficiency (%) | 2009 |
| Percentage and Total Area Covered by Protected Areas | Terrestrial - Protected Area (%) | 2017 |
| Percentage of Undernourished People | Prevalence of undernourishment (%) (3-year average) | 2015 |
| Proportion of Local Breeds Classified as Being At Risk, Not-at-risk or Unknown Level of Risk of Extinction | At Risk of Extinction | 2016 |
| Percentage of Key Biodiverisy Areas Protected | Estimate | 2016 |
| Protected Area Management Effectiveness | PA Assessed on Management Effectiveness (%) | 2015 |
| Protected Area Connectedness Index | Protected Area Connectedness Index | 2012 |
| Species Habitat Index | Species Habitat Index | 2014 |
| Species Protection Index (%) | Species Protection Index (%) | 2014 |
| Species Status Information Index | Value | 2014 |
| Total Wood Removals (roundwood, m3) | Total | 2014 |
| Trends in Forest Extent (Tree Cover) | Percentage of Tree Cover Loss | 2015 |
| Nitrogen Deposition Trends (kg N/ha/yr) | Nitrogen Deposition Trends (kg N/ha/yr) | 2030 |
| Trends in Pesticides Use | Use of pesticides (3-year average) | 2013 |
| Human Development Index | NA | 2018 |
| Average Harmonized Learning Outcomes Score | NA | 2015 |
| Gross Domestic Product (GDP) | NA | 2019 |
| Corruption Perception Index | NA | 2020 |
| Population | NA | 2018 |
There were a few instances of duplicated values which were double checked with the original dataset and the erroneous value removed. Examples include having two values for USA due to the separation of Hawaii in the original dataset. In these cases Hawaii was removed and the value referring to the rest of the states of the country was used instead. Additionally, Indicator 9, Region-based Marine Trophic Index, the mean of the regions was calculated per country, as countries such as Germany have multiple regions with distinct values.
To understand how valuation is spread across geographies, we counted the number of times each country’s or territories’ ISO code appeared in the corpus for both geography columns added in step 2. The result is the density of studies per country or territory and the density of funding institutions per country or territory for the entire corpus.
The external indicators were also joined onto the dataset to analyze the relationships between these socioeconomic indicators and the density of studies and funding institutions.
This process was also repeated with an additional filter that excluded any studies published before 2010.
To investigate the relationships between indicators and the number of valuation studies, we ran a pearson correlation analysis. The statistical analysis calculated the trends between the number of studies in each country or territory (Density of studies) and the number of studies funded in each country or territory (Density of institutions) and each of the indicators. The results are shown below.
Insignificant relationships are blank, significant relationships (P < 0.01) are shown with circles. The strength of the correlation corresponds to the size of the circle and the color represents positive (blue) or negative (red) trends.
Description of results
There is a very strong positive relationship between GDP and the density of both studies and institutions. GDP per capita does not exhibit the same strong correlation.
Density of studies is also strongly positively correlated to total wood removals, trends in pesticide use, population, nitrogen fertilizers, and water footprint index.
Density of institutions is also strongly positively correlated with total wood removals, and nitrogen fertilizers.
While most significant relationships are positive, there are two negative relationships present. Biodiversity inexactness index is negatively correlated with both the density of studies and institutions, and protected area management effectiveness is slightly negative correlated with the density of institutions.
knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure.png")
Pearson correlations of geographic valuation studies and indicators
The same pearson correlation analysis with the indicators was conducted on the log of the number of studies in each country or territory and the log of the number of studies funded from each country.
Description of results
Generally, the log density of studies and institutions show stronger and more correlations with the indicators. Within the log relationship, the protected area management effectiveness becomes significantly negatively correlated with both the density of studies and institutions. The biodiversity inexactness index also follows the same pattern.
For the log density of studies compared to the non-transformed density of studies, GDP becomes less strongly correlated, local breeds at risk of extinction becomes positively correlated, protected area connectedness index becomes strongly positively correlated, species protection index becomes positively correlated, as well as trends in forest extent, and nitrogen deposition trends.
For the log density of institutions compared to the non-transformed density of institutions, the human development index, average harmonized learning outcomes, corruption perception index, population, and local breeds at risk of extinction all become more strongly positively correlated, while the GDP becomes slightly less positively correlated. Additionally, the percentage of undernourished people becomes negatively correlated with the log density of studies, the species protection index and the species status information index become positively correlated, and finally the relationship to trends in pesticide use is no longer significant.
For both the log density of studies and institutions, the protected area connected index becomes strongly positively corrrelated.
knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure_log.png")
Pearson correlations of log geographic valuation studies and the indicators
The individual trends between each indicator and the number of studies and number of funding institutions are shown here for the entire corpus. For each indicator, there are two four panel figures with the top figure showing the trends with the raw values, and the bottom figure displaying the trends with log transformed x-axis values.
The dots represent countries within the dataset that have values for both the indicator and valuation atlas. The associated p value of the linear trend model is shown in the corner of the image in red. The trend line is shown in blue and the associated standard error in grey. Please note that the y axis is not necessarily consistent between the four boxes and may include values that aren’t actually found in the data to showcase the full extent of the standard error of the trend line.
There are some broad narratives emerge from this analysis:
Fewer studies are being carried out in countries with extreme poverty.
More ecosystem service/NCP assessment studies are being carried out where biodiversity and environmental degradation is higher and protection lower.
More studies are being carried out where natural capital is the highest and the conservation discourse is more advanced.
Some contradictions between these single variable narratives exist, such as more ecosystem services/NCP assessments carried out in countries with a lower protected area effectiveness index, and in countries with a higher species protection index, while these are significantly negatively correlated across countries. There is a knowledge gap and no robust understanding can be concluded on this.
For each figure the panels are arranged accordingly:
Density of studies (top-left) is the number of valuation studies for each country or territory in the corpus
Density of studies from 2010 (top-right) is the number of valuation studies for each country or territory in the corpus from 2010 to 2020
Density of institutions (bottom-left) is the number of valuation studies funded from each country or territory in the corpus
Density of institutions from 2010 (bottom-right) is the number of valuation studies funded from each country or territory in the corpus from 2010 to 2020
knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI.svg")
Relationships between density of valuation studies and associated institutions, and the human development index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the human development index
knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes.svg")
Relationships between density of valuation studies and associated institutions, and learning outcomes
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes_log.svg")
Log relationships between density of valuation studies and associated insitutions, and learning outcomes
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP.svg")
Relationships between density of valuation studies and associated institutions, and GDP
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_log.svg")
LLog relationships between density of valuation studies and associated insitutions, and GDP
knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI.svg")
Relationships between density of valuation studies and associated institutions, and the corruption perception index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the corruption perception index
knitr::include_graphics("Outputs/Figures_Corpus_individual/POP.svg")
Relationships between density of valuation studies and associated institutions, and population
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/POP_log.svg")
Log relationships between density of valuation studies and associated insitutions, and population
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita.svg")
Relationships between density of valuation studies and associated institutions, and GDP per capita
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita_log.svg")
Log relationships between density of valuation studies and associated insitutions, and GDP per capita
knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC.svg")
Relationships between density of valuation studies and associated institutions, and forest area under FSC certification
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC_log.svg")
Log relationships between density of valuation studies and associated insitutions, and forest area under FSC certification
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index.svg")
Relationships between density of valuation studies and associated institutions, and the biodiversity habitat index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the biodiversity habitat index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index.svg")
Relationships between density of valuation studies and associated institutions, and the biodiversity intactness index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the biodiversity intactness index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita.svg")
Relationships between density of valuation studies and associated institutions, and the biocapacity per capita
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the biocapacity per capita
knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint.svg")
Relationships between density of valuation studies and associated institutions, and the ecological footprint per capita
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the ecological footprint per capita
knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area.svg")
Relationships between density of valuation studies and associated institutions, and forest area
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area_log.svg")
Log relationships between density of valuation studies and associated insitutions, and forest area
knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint.svg")
Relationships between density of valuation studies and associated institutions, and the water footprint
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the water footprint
knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production.svg")
Relationships between density of valuation studies and associated institutions, and inland fishery production
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production_log.svg")
Log relationships between density of valuation studies and associated insitutions, and inland fishery production
knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index.svg")
Relationships between density of valuation studies and associated institutions, and the region-based marine trophic index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index_log.svg")
Log relationships between density of valuation studies and associated insitutions, and the region-based marine trophic index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers.svg")
Relationships between density of valuation studies and associated institutions, and nitrogen fertization
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers_log.svg")
Log relationships between density of valuation studies and associated insitutions, and nitrogen fertization
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency.svg")
Relationships between density of valuation studies and associated institutions, and nitrogen use efficiency
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency_log.svg")
Log relationships between density of valuation studies and associated insitutions, and nitrogen use efficiency
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected.svg")
Relationships between density of valuation studies and associated institutions, and percentage of area covered by protected areas
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected_log.svg")
Log relationships between density of valuation studies and associated insitutions, and percentage of area covered by protected areas
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people.svg")
Relationships between density of valuation studies and associated institutions, and percentage of undernourished people
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people_log.svg")
Log relationships between density of valuation studies and associated insitutions, and percentage of undernourished people
knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds.svg")
Relationships between density of valuation studies and associated institutions, and local breeds at risk of extinction
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds_log.svg")
Log relationships between density of valuation studies and associated insitutions, and local breeds at risk of extinction
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage.svg")
Relationships between density of valuation studies and associated institutions, and PA of key biodiverisy area coverage
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage_log.svg")
Log relationships between valuation atlas and PA of key biodiverisy area coverage
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness.svg")
Relationships between density of valuation studies and associated institutions, and protected area management effectiveness
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness_log.svg")
Log relationships between valuation atlas and protected area management effectiveness
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness.svg")
Relationships between density of valuation studies and associated institutions, and the protected area connectedness index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness_log.svg")
Log relationships between valuation atlas and the protected area connectedness index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index.svg")
Relationships between density of valuation studies and associated institutions, and the species habitat index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index_log.svg")
Log relationships between valuation atlas and the species habitat index
knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index.svg")
Relationships between density of valuation studies and associated institutions, and the species protection index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index_log.svg")
Log relationships between valuation atlas and the species protection index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status.svg")
Relationships between density of valuation studies and associated institutions, and the species status information index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status_log.svg")
Log relationships between valuation atlas and the species status information index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals.svg")
Relationships between density of valuation studies and associated institutions, and total wood removals
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals_log.svg")
Log relationships between valuation atlas and total wood removals
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_forest_extent.svg")
Relationships between density of valuation studies and associated institutions, and trends in forest extent
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_forest_extent_log.svg")
Log relationships between valuation atlas and trends in forest extent
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends.svg")
Relationships between density of valuation studies and associated institutions, and nitrogen deposition trends
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends_log.svg")
Log relationships between valuation atlas and nitrogen deposition trends
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_pesticides_use.svg")
Relationships between density of valuation studies and associated institutions, and trends in pesticides use
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_pesticides_use_log.svg")
Log relationships between valuation atlas and trends in pesticides use
Datasets
Altinok, N., N. Angrist and H.A. Patrinos. 2018. “Global data set on education quality (1965-2015).” World Bank Policy Research Working Paper No. 8314. Washington, DC. https://ourworldindata.org/grapher/learning-outcomes-vs-gdp-per-capita
Corruption Perception Index (2020) by Transparency International is licensed under CC-BY-ND 4.0 https://www.transparency.org/en/cpi/2020/index/
FAO. FAOSTAT. Annual Population. Latest update: 16/12/2019. (Accessed 24/03/2021). http://www.fao.org/faostat/en/#data/OA
FAO. FAOSTAT. Macro Indicators: Gross Domestic Product - Values US$. Latest update: 27/04/2021. (Accessed 02/09/2021). http://www.fao.org/faostat/en/#data/MK
Global Administrative Areas (version 3.6). University of California, Berkeley. Available online: https://gadm.org/
IPBES task force on knowledge and data (2020) IPBES Core Indicators - Harmonized Dataset, technical support unit on knowledge and data, Frankfurt, Germany. DOI: 10.5281/zenodo.4439411
IPBES Technical Support Unit on Knowledge and Data. (2020). IPBES regions and sub-regions (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3928281
ISO 3166-1:2020, Codes for the representation of names of countries and their subdivisions — Part 1: Country codes
United Nations Development Programme. Human Development Reports. Human Development Index (HDI). (Accessed 24/03/2021). http://hdr.undp.org/en/indicators/137506#
R packages
Claus O. Wilke (2019). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.0.0. https://CRAN.R-project.org/package=cowplot
Frank E Harrell, et al. (2020). Hmisc: Harrell Miscellaneous. R package version 4.4-1. https://CRAN.R-project.org/package=Hmisc
Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl
Michael C. J. Kao, Markus Gesmann and Filippo Gheri (2020). FAOSTAT: Download Data from the FAOSTAT Database. R package version 2.2.1. https://CRAN.R-project.org/package=FAOSTAT
Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009
Wei T, Simko V (2021). R package ‘corrplot’: Visualization of a Correlation Matrix. (Version 0.90), https://github.com/taiyun/corrplot.
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686